Can ChatGPT Be Addictive? A Call to Examine the Shift from Support to Dependence in AI Conversational Large Language Models
Open accessAbstract
The rapid rise of ChatGPT has introduced a transformative tool that enhances productivity, communication, and task automation across industries. However, concerns are emerging regarding the addictive potential of AI large language models. This paper explores how ChatGPT fosters dependency through key features such as personalised responses, emotional validation, and continuous engagement. By offering instant gratification and adaptive dialogue, ChatGPT may blur the line between AI and human interaction, creating pseudosocial bonds that can replace genuine human relationships. Additionally, its ability to streamline decision-making and boost productivity may lead to over-reliance, reducing users' critical thinking skills and contributing to compulsive usage patterns. These behavioural tendencies align with known features of addiction, such as increased tolerance and conflict with daily life priorities. This viewpoint paper highlights the need for further research into the psychological and social impacts of prolonged interaction with AI tools like ChatGPT.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A substantial body of scholarship has identified the asymmetry of AI companionship as a mechanism through which AI companions may foster unrealistic relational expectations, displace effortful human intimacy, and normalize one-sided emotional dynamics.
“A substantial body of scholarship has identified this asymmetry as a mechanism through which AI companions may foster unrealistic relational expectations, displace effortful human intimacy, and normalize one-sided emotional dynamics (Turkle, 2011; Malfacini, 2025; Yankouskaya et al., 2025).”
From Intimate Strangers by Design: A Uses and Gratifications Analysis of AI Companionship (2026) · cites this paper as Yankouskaya et al., 2025
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredExisting research on chatbots and relational AI has reflected a risk-oriented framing.
“Existing research on chatbots and relational AI has reflected a risk-oriented framing (Chianella et al., 2021; Yankouskaya et al., 2025).”
From Intimate Strangers by Design: A Uses and Gratifications Analysis of AI Companionship (2026) · cites this paper as Yankouskaya et al., 2025
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior
Deci, E. L., & Ryan, R. M. (2000) · Psychological Inquiry · doi:10.1207/s15327965pli1104_01 · Cited by 34,210 · cited 1× · see citing works
“Such platforms tap into the inherent link between self-specificity and reward sensitivity, offering experiences that feel tailored and meaningful [30].”
Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810 · cited 1× · see citing works
“In behavioural economics, high cognitive load is often mitigated by “heu-ristics” or mental shortcuts [87].”
Ryan, R. M., & Deci, E. L. (2000) · American Psychologist · doi:10.1037//0003-066x.55.1.68 · Cited by 27,815 · see citing works
Internet Addiction: The Emergence of a New Clinical Disorder
YOUNG, K. S. (1998) · CyberPsychology & Behavior · doi:10.1089/cpb.1998.1.237 · Cited by 5,513 · see citing works
Mass Communication and Para-Social Interaction
Horton, D., & Richard Wohl, R. (1956) · Psychiatry · doi:10.1080/00332747.1956.11023049 · Cited by 4,380 · see citing works
On seeing human: A three-factor theory of anthropomorphism
Epley, N., Waytz, A., & Cacioppo, J. T. (2007) · Psychological Review · doi:10.1037/0033-295x.114.4.864 · Cited by 4,069 · cited 1× · see citing works
“Studies in media psychology suggest that people tend to blur the boundaries between digital and real-world interactions [54, 55] which may be rooted in the human inclination to anthropomorphise or attribute human-like characteristics to non-human entities [56–58].”
An Experimental Study of Apparent Behavior
Heider, F., & Simmel, M. (1944) · The American Journal of Psychology · doi:10.2307/1416950 · Cited by 3,340 · see citing works
A “components” model of addiction within a biopsychosocial framework
Griffiths, M. (2005) · Journal of Substance Use · doi:10.1080/14659890500114359 · Cited by 3,088 · see citing works
Self-referential processing in our brain—A meta-analysis of imaging studies on the self
Northoff, G., Heinzel, A., de Greck, M., Bermpohl, F., Dobrowolny, H., & Panksepp, J. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.12.002 · Cited by 2,792 · see citing works
Experimental evidence on the productivity effects of generative artificial intelligence
Noy, S., & Zhang, W. (2023) · Science · doi:10.1126/science.adh2586 · Cited by 2,150 · cited 1× · see citing works
“For example, recent studies have shown a 40% improvement in writing task productivity and an 18% increase in output quality [69].”
Brand, M., Wegmann, E., Stark, R., Müller, A., Wölfling, K., Robbins, T. W., & Potenza, M. N. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.06.032 · Cited by 2,126 · cited 1× · see citing works
“…delve into classical models of behavioural addiction, such as Griffiths’ components model [110] or the Interaction of Person-Affect-Cognition-Execution (I-PACE) model [20], future work might consider how these frameworks could be adapted to capture the unique dynamics of AI-mediated interactions.”
Knee-deep in the big muddy: a study of escalating commitment to a chosen course of action
Staw, B. M. (1976) · Organizational Behavior and Human Performance · doi:10.1016/0030-5073(76)90005-2 · Cited by 2,061 · cited 1× · see citing works
“This reflects the concept of escalation of commitment , where individuals continue to invest excessive time and energy into work, even when it leads to negative outcomes, driven by a desire to justify previous efforts and maximise perceived productivity gains [80].”
Zhai, C., Wibowo, S., & Li, L. D. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00316-7 · Cited by 1,567 · see citing works
Cortical midline structures and the self
Northoff, G., & Bermpohl, F. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.01.004 · Cited by 1,446 · see citing works
The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents1
Komiak, S. Y. X., & Benbasat, I. (2006) · MIS Quarterly · doi:10.2307/25148760 · Cited by 1,374 · see citing works
Affective interactions using virtual reality: the link between presence and emotions
Riva, G., Mantovani, F., Capideville, C. S., Preziosa, A., Morganti, F., Villani, D., Gaggioli, A., Botella, C., & Alcañiz, M. (2007) · CyberPsychology & Behavior · doi:10.1089/cpb.2006.9993 · Cited by 1,111 · see citing works
My Chatbot Companion - a Study of Human-Chatbot Relationships
Skjuve, M., Følstad, A., Fostervold, K. I., & Brandtzaeg, P. B. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102601 · Cited by 797 · cited 2× · see citing works
“Some research suggests that interactions between humans and social chatbots may mimic the formation of human-to-human relationships [3, 41, 42].”
A systematic review including meta-analysis of work environment and burnout symptoms
Aronsson, G., Theorell, T., Grape, T., Hammarström, A., Hogstedt, C., Marteinsdottir, I., Skoog, I., Träskman-Bendz, L., & Hall, C. (2017) · BMC Public Health · doi:10.1186/s12889-017-4153-7 · Cited by 789 · cited 1× · see citing works
“This can lead to burnout, exhaustion, and a deteriorating work-life balance, much like how addiction leads to diminishing returns and increased harm over time [74–76].”
Nesi, J., & Prinstein, M. J. (2015) · Journal of Abnormal Child Psychology · doi:10.1007/s10802-015-0020-0 · Cited by 758 · cited 1× · see citing works
“This aligns with research on the problematic use of social media driven by the need for social validation and reassurance [103] and leading to fear of missing out [104] and procrastination [105].”
Building a Stronger CASA: Extending the Computers Are Social Actors Paradigm
Gambino, A., Fox, J., & Ratan, R. (2020) · Human-Machine Communication · doi:10.30658/hmc.1.5 · Cited by 626 · see citing works
Wach, K., Duong, C. D., Ejdys, J., Kazlauskaitė, R., Korzynski, P., Mazurek, G., Paliszkiewicz, J., & Ziemba, E. (2023) · Entrepreneurial Business and Economics Review · doi:10.15678/eber.2023.110201 · Cited by 584 · cited 2× · see citing works
“Furthermore, compared with domain-specific models like LaMDA (focused on dialogue nuances) [4] or Replika, ChatGPT’s training data encompasses a wide range of topics, enabling it to perform effectively in fields such as healthcare, education, programming, and customer support [5, 6].”
Taking Account of Time Lags in Causal Models
Gollob, H. F., & Reichardt, C. S. (1987) · Child Development · doi:10.2307/1130293 · Cited by 572 · see citing works
Attribution in Social and Parasocial Relationships
PERSE, E. M., & RUBIN, R. B. (1989) · Communication Research · doi:10.1177/009365089016001003 · Cited by 559 · see citing works
Abbas, M., Jam, F. A., & Khan, T. I. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00444-7 · Cited by 531 · cited 1× · see citing works
“As decision-making is increasingly delegated to AI systems, users gradually bypass cognitive processes critical for judgment [88].”
Alon-Barkat, S., & Busuioc, M. (2023) · Journal of Public Administration Research and Theory · doi:10.1093/jopart/muac007 · Cited by 469 · cited 1× · see citing works
“This phenomenon, known as automation bias , causes users to default to AI guidance, diminishing their ability to critically evaluate or question the information presented [90].”
User Experiences of Social Support From Companion Chatbots in Everyday Contexts: Thematic Analysis
Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero, E., & Loggarakis, A. (2020) · Journal of Medical Internet Research · doi:10.2196/16235 · Cited by 461 · cited 3× · see citing works
“For instance, unlike Replika, which primarily caters to personal emotional support, or BlenderBot, optimised for open-domain chit-chat, ChatGPT scales effortlessly from casual interaction to high-stakes professional tasks [3].”
Laestadius, L., Bishop, A., Gonzalez, M., Illenčík, D., & Campos-Castillo, C. (2024) · New Media & Society · doi:10.1177/14614448221142007 · Cited by 420 · see citing works
Hu, B., Mao, Y., & Kim, K. J. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107760 · Cited by 228 · see citing works
Voices in and of the machine: Source orientation toward mobile virtual assistants
Guzman, A. L. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.08.009 · Cited by 199 · see citing works
Huang, S., Lai, X., Ke, L., Li, Y., Wang, H., Zhao, X., Dai, X., & Wang, Y. (2024) · Psychology Research and Behavior Management · doi:10.2147/prbm.s440889 · Cited by 196 · see citing works
Dergaa, I., Ben Saad, H., Glenn, J. M., Amamou, B., Ben Aissa, M., Guelmami, N., Fekih-Romdhane, F., & Chamari, K. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1259845 · Cited by 93 · see citing works
Flagged first, then by citations
Research works printed but not linked: 64
- Ebert C, Louridas P. Generative AI for software practitioners. IEEE Softw. 2023;40(4):30-8. https:// doi. org/ 10. 1109/ MS. 2023. 32658 77. · DOI · Paper page
- Thoppilan R, De Freitas D, Hall J, Shazeer NM, Kulshreshtha et al. LaMDA: language models for dialog applications. arXiv. 2022. https:// arxiv. org/ abs/ 2201. 08239. Accessed 20 Jan 2025.
- Xie Y, Zhao S, Zhou P, Liang C. Understanding contin- ued use intention of AI assistants. J Comput Inform Syst. 2023;63(6):1424-37. https:// doi. org/ 10. 1080/ 08874 417. 2023. 21671 34. · DOI
- Björling EA, Ling H, Bhatia S, Matarrese J. Sharing stressors with a social robot prototype: what embodiment do adolescents prefer? Int J Child Comput Interact. 2021;28:1-11. https:// doi. org/ 10. 1016/j. ijcci. 2021. 100252. · DOI
- Liu AR, Pataranutaporn P, Turkle S, Maes P. Chatbot companion- ship: a mixed-methods study of companion chatbot usage pat- terns and their relationship to loneliness in active users. arXiv preprint. 2024; arXiv: 2410. 21596.
- Ng YL. Exploring the association between use of conversational artificial intelligence and social capital: survey evidence from Hong Kong. New Media Soc. 2024;26(3):1429-44. https:// doi. org/ 10. 1177/ 14614 44822 10740 47. · DOI
- Campanella S. Addictive behaviors: decades of research, but still so many questions! Front Psychol. 2024;15:1485118. https:// doi. org/ 10. 3389/ fpsyg. 2024. 14851 18. · DOI
- Sussman S. Workaholism: a review. J Addict Res Ther. 2012;6(1):4120. https:// doi. org/ 10. 4172/ 2155-6105. S6-001. · DOI
- Alavi SS, Ferdosi M, Jannatifard F, Eslami M, Alaghemandan H, Setare M. Behavioral addiction versus substance addiction: cor- respondence of psychiatric and psychological views. Int J Prev Med. 2012;3(4):290-4.
- Ding K, Shen Y, Liu Q, Li H. The effects of digital addiction on brain function and structure of children and adolescents: a scop- ing review. Healthcare (Basel). 2023;12(1):15. https:// doi. org/ 10. 3390/ healt hcare 12010 015. · DOI
- Heilig M, MacKillop J, Martinez D, et al. Addiction as a brain disease revised: why it still matters, and the need for consilience. Neuropsychopharmacology. 2021;46(11):1715-23. https:// doi. org/ 10. 1038/ s41386-020-00950-y. · DOI
- Griffiths MD, Kuss DJ, Billieux J, Pontes H. The evolution of Internet addiction: a global perspective. Addict Behav. 2016;53:193-5. https:// doi. org/ 10. 1016/j. addbeh. 2015. 11. 001. · DOI
- Salah M, Abdelfattah F, Al HH. The good, the bad, and the GPT: reviewing the impact of generative artificial intelligence on psy- chology. Curr Opin Psychol. 2024;59: 101872. https:// doi. org/ 10. 1016/j. copsyc. 2024. 101872. · DOI
- Sundar SS, Lee E-J. Rethinking communication in the era of artificial intelligence. Hum Commun Res. 2022;48(3):379-85. https:// doi. org/ 10. 1093/ hcr/ hqac0 14. · DOI
- Qin P, Liu Y, Shi J, Wang Y, Duncan N, Gong Q, Weng X, Northoff G. Dissociation between anterior and posterior corti- cal regions during self-specificity and familiarity: a combined fMRI-meta-analytic study. Hum Brain Mapp. 2012;33(1):154- 64. https:// doi. org/ 10. 1002/ hbm. 21201. · DOI
- Northoff G. Is the self a higher-order or fundamental function of the brain? The "basis model of self-specificity" and its encoding by the brain's spontaneous activity. Cogn Neurosci. 2016;7(1- 4):203-22. https:// doi. org/ 10. 1080/ 17588 928. 2015. 11118 68. · DOI
- Hillman JG, Fowlie DI, MacDonald TK. Social Verification Theory: a new way to conceptualize validation, dissonance, and belonging. Pers Soc Psychol Rev. 2023;27(3):309-31. https:// doi. org/ 10. 1177/ 10888 68322 11383 84. · DOI
- Schoeller F, Christov-Moore L, Lynch C, Diot T, Reggente N. Predicting individual differences in peak emotional response. PNAS Nexus. 2024;3(3):pgae066. https:// doi. org/ 10. 1093/ pnasn exus/ pgae0 66. · DOI
- Stets JE, Burke PJ. Self-esteem and identities. Sociol Perspect. 2014;57(4):409-33. https:// doi. org/ 10. 1177/ 07311 21414 536141. · DOI
- Nakamura J, Csikszentmihalyi M. The concept of flow. In: Flow and the foundations of positive psychology. Dordrecht: Springer; 2014. p. 239-63. https:// doi. org/ 10. 1007/ 978-94-017-9088-8_ 16. · DOI · Paper page
- Gold J, Ciorciari J. A review on the role of the neurosci- ence of flow states in the modern world. Behav Sci (Basel). 2020;10(9):137. https:// doi. org/ 10. 3390/ bs100 90137. · DOI
- Finneran CM, Zhang P. A person-artefact-task (PAT) model of flow antecedents in computer-mediated environments. Int J Hum Comput Stud. 2003;59(4):475-96. https:// doi. org/ 10. 1016/ S1071-5819(03) 00112-5. · DOI
- Youn S, Jin SV. In AI we trust?" The effects of parasocial interac- tion and technopian versus luddite ideological views on chatbot- based customer relationship management in the emerging "feel- ing economy. Comput Hum Behav. 2021;119: 106721. https:// doi. org/ 10. 1016/j. chb. 2021. 106721. · DOI
- Lotun S, Lamarche VM, Matran-Fernandez A, et al. People per- ceive parasocial relationships to be effective at fulfilling emo- tional needs. Sci Rep. 2024;14:8185. https:// doi. org/ 10. 1038/ s41598-024-58069-9. · DOI
- Giles DC, Maltby J. The role of media figures in adolescent development: relations between autonomy, attachment, and inter- est in celebrities. Pers Individ Differ. 2004;36(4):813-22. https:// doi. org/ 10. 1016/ S0191-8869(03) 00154-5. · DOI
- Skumanich SA, Kintsfather DP. Individual media dependency relations within television shopping programming: a causal model reviewed and revised. Commun Res. 1998;25(2):200-19. https:// doi. org/ 10. 1177/ 00936 50980 25002 004. · DOI
- Giles DC. Parasocial interaction: a review of the literature and a model for future research. Media Psychol. 2002;4(3):279-305. https:// doi. org/ 10. 1207/ S1532 785XM EP0403_ 04. · DOI · Paper page
- Dibble JL, Hartmann T, Rosaen SF. Parasocial interaction and parasocial relationship: conceptual clarification and a critical assessment of measures. Hum Commun Res. 2016;42(1):21-44. https:// doi. org/ 10. 1111/ hcre. 12063. · DOI
- Ferrari F, Paladino MP, Jetten J. Blurring human-machine dis- tinctions: anthropomorphic appearance in social robots as a threat to human distinctiveness. Int J Soc Robot. 2016;8(3):287- 302. https:// doi. org/ 10. 1007/ s12369-016-0338-y. · DOI
- Krach S, Hegel F, Wrede B, Sagerer G, Binkofski F, Kircher T. Can machines think? Interaction and perspective taking with robots investigated via fMRI. PLoS ONE. 2008;3(7): e2597. https:// doi. org/ 10. 1371/ journ al. pone. 00025 97. · DOI
- Rubin AM, Perse EM, Powell RA. Loneliness, parasocial inter- action, and local television news viewing. Hum Commun Res. 1985;12(2):155-80. https:// doi. org/ 10. 1111/j. 1468-2958. 1985. tb000 71.x. · DOI
- Govindaraju V. Media psychology approaches in digitalization and human communication. In: Promoting diversity, equity, and inclusion in language learning environments. IGI Global Infor- mation Science Reference; 2023. pp. 193-202. https:// doi. org/ 10. 4018/ 978-1-6684-3632-5. ch012. · DOI
- Nicolas S, Agnieszka W. The personality of anthropomorphism: how the need for cognition and the need for closure define atti- tudes and anthropomorphic attributions toward robots. Comput Hum Behav. 2021;122: 106841. https:// doi. org/ 10. 1016/j. chb. 2021. 106841. · DOI
- Nazar M, Alam MM, Yafi E, Su'ud MM. A systematic review of human-computer interaction and explainable artificial intel- ligence in healthcare with artificial intelligence techniques. IEEE Access. 2021;9:153316-48. https:// doi. org/ 10. 1109/ ACCESS. 2021. 31278 81. · DOI · Paper page
- Cook KS, Cheshire C, Rice ERW, Nakagawa S. Social exchange theory. In: DeLamater J, Ward A, editors. Handbook of social psychology. 2nd ed. Berlin: Springer; 2013. p. 61-88. https:// doi. org/ 10. 1007/ 978-94-007-6772-0_3. · DOI
- Feeney BC, Collins NL. A new look at social support: a theo- retical perspective on thriving through relationships. Pers Soc Psychol Rev. 2015;19(2):113-47. https:// doi. org/ 10. 1177/ 10888 68314 544222. · DOI · Paper page
- Levy N, Harmon-Jones C, Harmon-Jones E. Dissonance and dis- comfort: does a simple cognitive inconsistency evoke a negative affective state? Motiv Sci. 2018;4(2):95-108. https:// doi. org/ 10. 1037/ mot00 00079. · DOI
- Hobfoll SE. Conservation of resources theory: Its implication for stress, health, and resilience. In: Folkman S, editor. The Oxford handbook of stress, health, and coping. Oxford: Oxford Univer- sity Press; 2011. p. 127-47.
- Vaillant GE. Ego mechanisms of defense and personality psy- chopathology. J Abnorm Psychol. 1994;103(1):44-50. https:// doi. org/ 10. 1037/ 0021-843x. 103.1. 44. · DOI
- Coughlin SS. Recall bias in epidemiologic studies. J Clin Epide- miol. 1990;43(1):87-91. https:// doi. org/ 10. 1016/ 0895-4356(90) 90060-3. · DOI · Paper page
- King R. Cognitive therapy of depression. Aaron Beck, John Rush, Brian Shaw, Gary Emery. New York: Guilford, 1979. Aust N Z J Psychiatry. 2002;36(2):272-275. https:// doi. org/ 10. 1046/j. 1440-1614. 2002. t01-4-01015.x · DOI
- Gu Z, Chupradit S, Ku KY, Nassani AA, Haffar M. Impact of employees' workplace environment on employees' performance: a multi-mediation model. Front Public Health. 2022;10: 890400. https:// doi. org/ 10. 3389/ fpubh. 2022. 890400. · DOI
- Liu Y, Yang Y, Bai X, Chen Y, Mo L. Do immediate exter- nal rewards really enhance intrinsic motivation? Front Psychol. 2022;13: 853879. https:// doi. org/ 10. 3389/ fpsyg. 2022. 853879. · DOI
- Woolley K, Fishbach A. Immediate rewards predict adherence to long-term goals. Pers Soc Psychol Bull. 2017;43(2):151-62. https:// doi. org/ 10. 1177/ 01461 67216 676480. · DOI · Paper page
- Gao Z, Wang H, Lu C, et al. The neural basis of delayed gratifi- cation. Sci Adv. 2021;7(49):eabg6611. https:// doi. org/ 10. 1126/ sciadv. abg66 11. · DOI
- Bromberg-Martin ES, Matsumoto M, Hikosaka O. Dopamine in motivational control: rewarding, aversive, and alerting. Neuron. 2010;68(5):815-34. https:// doi. org/ 10. 1016/j. neuron. 2010. 11. 022. · DOI · Paper page
- Yang X, Qiu D, Lau MCM, Lau JTF. The mediation role of work- life balance stress and chronic fatigue in the relationship between workaholism and depression among Chinese male workers in Hong Kong. J Behav Addict. 2020;9(2):483-90. https:// doi. org/ 10. 1556/ 2006. 2020. 00026. · DOI
- Andreassen CS. Workaholism: an overview and current status of the research. J Behav Addict. 2014;3(1):1-11. https:// doi. org/ 10. 1556/ JBA.2. 2013. 017. · DOI
- Molino M, Cortese CG, Ghislieri C. Daily effect of recovery on exhaustion: a cross-level interaction effect of workaholism. Int J Environ Res Public Health. 2018;15(9):1920. https:// doi. org/ 10. 3390/ ijerp h1509 1920. · DOI
- Morkevičiūtė M, Endriulaitienė A. The role of an individual and a situation in explaining work addiction: disclosing complex rela- tions. Int J Environ Res Public Health. 2023;20(5):4560. https:// doi. org/ 10. 3390/ ijerp h2005 4560. · DOI
- Pluut H, Wonders J. Not able to lead a healthy life when you need it the most: dual role of lifestyle behaviors in the association of blurred work-life boundaries with well-being. Front Psychol. 2020;11: 607294. https:// doi. org/ 10. 3389/ fpsyg. 2020. 607294. · DOI
- Starcevic V, Berle D. Cyberchondria: towards a better under- standing of excessive health-related Internet use. Expert Rev Neurother. 2013;13(2):205-13. https:// doi. org/ 10. 1586/ ern. 12. 162. · DOI · Paper page
- Fechner HB, Schooler LJ, Pachur T. Cognitive costs of decision- making strategies: a resource demand decomposition analysis with a cognitive architecture. Cognition. 2018;170:102-22. https:// doi. org/ 10. 1016/j. cogni tion. 2017. 09. 003. · DOI
- Skagerlund K, Forsblad M, Tinghög G, Västfjäll D. Decision- making competence and cognitive abilities: which abilities mat- ter? J Behav Decis Mak. 2022;35(1): e2242. https:// doi. org/ 10. 1002/ bdm. 2242. · DOI · Paper page
- Kool W, McGuire JT, Rosen ZB, Botvinick MM. Decision mak- ing and the avoidance of cognitive demand. J Exp Psychol Gen. 2010;139(4):665-82. https:// doi. org/ 10. 1037/ a0020 198. · DOI · Paper page
- Vicente L, Matute H. Humans inherit artificial intelligence biases. Sci Rep. 2023;13:15737. https:// doi. org/ 10. 1038/ s41598-023-42384-8. · DOI · Paper page
- Wiczorek R, Meyer J. Effects of trust, self-confidence, and feedback on the use of decision automation. Front Psychol. 2019;10:519. https:// doi. org/ 10. 3389/ fpsyg. 2019. 00519. · DOI · Paper page
- Jussupow E, Spohrer K, Heinzl A, Gawlitza J. Augmenting medi- cal decision-making? How delegation to AI influences decision- making under uncertainty. Inf Syst Res. 2021;32:713-35. https:// doi. org/ 10. 1287/ isre. 2020. 0980. · DOI · Paper page
- Sele D, Chugunova M. Putting a human in the loop: Increasing uptake, but decreasing accuracy of automated decision-making. PLoS ONE. 2024;19: e0298037. https:// doi. org/ 10. 1371/ journ al. pone. 02980 37. · DOI
- Dwivedi YK, Kshetri N, Hughes L, Slade EL, Jeyaraj A, Kar AK, et al. Opinion Paper: "So what if ChatGPT wrote it?" Multidis- ciplinary perspectives on opportunities, challenges and implica- tions of generative conversational AI for research, practice and policy. Int J Inf Manage. 2023;71: 102642. https:// doi. org/ 10. 1016/j. ijinf omgt. 2023. 102642. · DOI · Paper page
- Liaw W, Chavez S, Pham C, Tehami S, Govender R. The haz- ards of using ChatGPT: a call to action for medical education researchers. PRiMER. 2023;7:27. https:// doi. org/ 10. 22454/ PRiMER. 2023. 295710. · DOI
- Hasanein AM, Sobaih AEE. Drivers and consequences of Chat- GPT use in higher education: key stakeholder perspectives. Eur J Investig Health Psychol Educ. 2023;13:2599-614. https:// doi. org/ 10. 3390/ ejihp e1311 0181. · DOI
- Haman M, Školník M. Behind the ChatGPT hype: are its suggestions contributing to addiction? Ann Biomed Eng. 2023;51:1128-9. https:// doi. org/ 10. 1007/ s10439-023-03201-5. · DOI
- Yu SC, Chen HR, Yang YW. Development and valida- tion of the problematic ChatGPT Use Scale: a preliminary report. Curr Psychol. 2024;43:1-13. https:// doi. org/ 10. 1007/ s12144-024-06259-z. · DOI
Cited by
Sorted by citations
Yankouskaya, A., Babiker, A., Rizvi, S., Alshakhsi, S., Liebherr, M., & Ali, R. (2025) · ACM Transactions on the Web · doi:10.1145/3765895 · Cited by 32 · see citing works
Self-citationKhoso, A. K., Honggang, W., Younas, M., & Salam El-Dakhs, D. A. (2025) · Journal of Visualized Experiments · doi:10.3791/69637 · Cited by 13 · see citing works
Characterizing Delusional Spirals through Human-LLM Chat Logs
Moore, J., Mehta, A., Agnew, W., Anthis, J. R., Louie, R., Mai, Y., Yin, P., Cheng, M., Paech, S. J., Klyman, K., et al. (2026) · Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3805689.3806443 · Cited by 15 · see citing works
A scoping review on the mental health harms of LLM-based chatbots
Diel, A., Torous, J., Cuijpers, P., Kleesiek, J., Nensa, F., Weber, N., Faust, F., Lalgi, T. J., Mellis, F. S., Teufel, M., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-03054-x · Cited by 4 · see citing works
Untitled work
Eom, D., Renner, J., & Chinn, S. (2026) · arXiv (preprint)
- Authors
- Yankouskaya, A., Liebherr, M., & Ali, R.
- Affiliations
- Bournemouth University (GB)
- University of Duisburg-Essen (DE)
- Hamad bin Khalifa University (QA)
- Corresponding author
- Yankouskaya, Ala
- Venue
- Human-Centric Intelligent Systems
- Published
- 2025-02-17
- Volume
- 5
- Issue
- 1
- Pages
- 77-89
- ISSN
- 2667-1336 (ISSN-L)
- OpenAlex
- W4407637219
- Cited by
- 100
- References
- 31
- Citations by year
- 2026: 72 · 2025: 28
- FWCI
- 172.99
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Human-Centric Intelligent Systems (publishedVersion, cc-by)
- Bournemouth University Research Online (Bournemouth University) (acceptedVersion)
- Universitätsbibliographie, Universität Duisburg-Essen (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Qatar National Research Fund (NPRP 14C-0916–210015)
- Topics
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- ChatGPT, AI dependency, large language models, overreliance on AI, critical thinking, emotional validation, tolerance (pharmacology and immunology), human-AI interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review